Abstract
An Echo State Network (ESN) can make multi-step predictions since it can process temporal information without the training difficulties encountered by conventional recurrent neural networks. an ESN is applied in this paper to make multistep predictions of solar irradiance, 30 minutes to 270 minutes into the future. the ESN is trained and tested using two performance metrics (correlation coefficient and mean squared error) on meteorological and solar data recorded at the National Renewable Energy Laboratory Solar Radiation Research Laboratory in Golden, Colorado. When feedback of target outputs is utilized, an improvement is seen for the first performance metric, while no significant change is seen for the second performance metric. Additionally, accuracy is observed to diminish significantly as the time horizon for the predictions increases. © 2009 IEEE.
Recommended Citation
S. M. Ruffing and G. K. Venayagamoorthy, "Short to Medium Range Time Series Prediction of Solar Irradiance using an Echo State Network," 2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09, article no. 5352922, Institute of Electrical and Electronics Engineers, Dec 2009.
The definitive version is available at https://doi.org/10.1109/ISAP.2009.5352922
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Echo State Network (ESN); Solar irradiance; Time series multistep prediction
International Standard Book Number (ISBN)
978-142445098-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
09 Dec 2009
